I'm creating a churn model and would like to create a ratio (# customers / total transaction) for each merchant. About 70% of the data are NaNs (zero/zero).
I was wondering what I should impute for the 70% of the NaNs. I have other features and I don't like to delete the 70% of the data.
But if I impute 0s, the distribution would probably become different from ground truth since lower number means large transaction volume with a few customer. If I impute mean though, it'll also be different since the element (zero / zero) has no actions fundamentally.
I was about to impute -1 to distinguish the NaNs with non-NaNs. Would that make sense for a feature of binary classifier?